This Course has excellent explanations and advice on how to move your models into production and make sure they are reliables and don't lose accuracy over time. The course illustrates how to use the entire ecosystem on GCP that is impressive, quite happy with the explanation and the expert's advice.

Some modules were amazing and deserved more than 5 stars, other were a little too much. Specifically, everything was great until Kubeflow which I felt was not covered well and the demo not one thing was clear. Very bad demo! Please restructure that demo

The course provides insight into applying Machine Learning system in actual environment, with real-life examples and lab assistance.

The content and instructors are great. However it shoud not be a two weeks course, as the amount of topics is large.

The course is good. but i would appreciate if it would give more details and more coding exercises

Es un curso algo confuso que requiere bastante tiempo para comprender las tematicas

very very useful topics to improve and deploying machine learning for productions.

Course is a bit outdated. E.g., KubeFlow section and references to ML Engine.

Curso incriável, matérias bem explicadas, conteúdo super, detalhados.

Great. Ksonnet is not active. Vdo should talk about it.

It was Good Overall. Got something new to learn.

content is nice but too fast

Very Informative.

Good Experience

Excellent

GOOD

good

well

Some of the content was really interesting, particularly about the hybrid ML systems, dynamically training models, distributed training and data parallelism, but overall, the information was mostly high level with few exercises or labs to delve into actually designing and implementing this stuff. I wrote lots of notes and found myself asking 'how' a lot with no answer. It also felt like I was constantly being pitched to buy and use GCP services. And finally, to actually build a product off these tools that could be considered 'production', I don't think having a bunch of notebooks and random CLI commands to launch stuff is the most robust and traceable architecture. Great presenters though, really liked your style folks!

Overall rating is 3 out of 5, as I expected more of the initial line in the first course. The optional Kubeflow lab has issues, as the ksonnet apply command line halts. Also, the last lab was expected to allow the student to code more, as this is the only way to make a person to gain more insights on the architecture.

The module on hybrid systems was weak. The time it would take to cover the material would be prohibitive so why do the intro that then apologize for not having the time to explain the material. Leave it out...

While there is definitely some good and useful content in this course, not all of the material is useful. ~40% of the course felt like a sales pitch, at least to me.

I can't start next course after enrolling ML course ridiculous kindly start next course

This course include deep contexts about Machine Learning. But, It's somewhat boring.

Some errors in Kubeflow quicklabs.